Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (FMED). (4th July 2019)

• Record Type: Journal Article
• Authors: Shakeel, P. Mohamed; Baskar, S.; Sampath, R.; Jaber, Mustafa Musa
• Is Part Of: International journal of signal and imaging systems engineering. Volume 11:Number 5(2019)
• Page Start: 270
• Page End: 278
• Publication Date: 2019-07-04
• Subjects: echocardiography image segmentation -- ultrasound images -- FFANN -- feed forward artificial neural network -- FMED -- fuzzy multi-scale edge detection; Signal processing -- Periodicals; Imaging systems -- Periodicals; Information theory -- Periodicals; 621.3822
• Languages: English
• ISSNs: 1748-0698

Abstract

In the recent past Echocardiography image segmentation is one of the significant process describes about the segment out inner and outer walls or other parts of the organ boundaries. However, this kind of segmentation process is one of the difficult for physicians because of inexperience or subject specialists with the previous cases. To enhance the cardiac image segmentation accuracy and to minimise the segmentation time a machine learning method such as neural networks has been proposed in the segmentation process. In this research, feed forward artificial neural network (FFANN) has been utilised and fuzzy multi-scale edge detection (FMED) process has been applied to detect the segmented edges to define the detected texture boundary with the help of FFANN weights. An experimental result shows an efficient learning capacity of FFANN and this work deals with the segmentation of ultrasound images using MATLAB implementation.